Web scraping

There is a wealth of data on internet. How to scrape them and analyze them?

rvest

rvest is an R package written by Hadley Wickham which makes web scraping easy.

Scraping from webpage

Rank

  • Use SelectorGadget to highlight the element we want to scrape

  • Use the CSS selector to get the rankings

    # Use CSS selectors to scrap the rankings section
(rank_data_html <- html_nodes(webpage, '.text-primary'))
    ## {xml_nodeset (100)}
##  [1] <span class="lister-item-index unbold text-primary">1.</span>
##  [2] <span class="lister-item-index unbold text-primary">2.</span>
##  [3] <span class="lister-item-index unbold text-primary">3.</span>
##  [4] <span class="lister-item-index unbold text-primary">4.</span>
##  [5] <span class="lister-item-index unbold text-primary">5.</span>
##  [6] <span class="lister-item-index unbold text-primary">6.</span>
##  [7] <span class="lister-item-index unbold text-primary">7.</span>
##  [8] <span class="lister-item-index unbold text-primary">8.</span>
##  [9] <span class="lister-item-index unbold text-primary">9.</span>
## [10] <span class="lister-item-index unbold text-primary">10.</span>
## [11] <span class="lister-item-index unbold text-primary">11.</span>
## [12] <span class="lister-item-index unbold text-primary">12.</span>
## [13] <span class="lister-item-index unbold text-primary">13.</span>
## [14] <span class="lister-item-index unbold text-primary">14.</span>
## [15] <span class="lister-item-index unbold text-primary">15.</span>
## [16] <span class="lister-item-index unbold text-primary">16.</span>
## [17] <span class="lister-item-index unbold text-primary">17.</span>
## [18] <span class="lister-item-index unbold text-primary">18.</span>
## [19] <span class="lister-item-index unbold text-primary">19.</span>
## [20] <span class="lister-item-index unbold text-primary">20.</span>
## ...
    # Convert the ranking data to text
(rank_data <- html_text(rank_data_html))
    ##   [1] "1."   "2."   "3."   "4."   "5."   "6."   "7."   "8."   "9."   "10." 
##  [11] "11."  "12."  "13."  "14."  "15."  "16."  "17."  "18."  "19."  "20." 
##  [21] "21."  "22."  "23."  "24."  "25."  "26."  "27."  "28."  "29."  "30." 
##  [31] "31."  "32."  "33."  "34."  "35."  "36."  "37."  "38."  "39."  "40." 
##  [41] "41."  "42."  "43."  "44."  "45."  "46."  "47."  "48."  "49."  "50." 
##  [51] "51."  "52."  "53."  "54."  "55."  "56."  "57."  "58."  "59."  "60." 
##  [61] "61."  "62."  "63."  "64."  "65."  "66."  "67."  "68."  "69."  "70." 
##  [71] "71."  "72."  "73."  "74."  "75."  "76."  "77."  "78."  "79."  "80." 
##  [81] "81."  "82."  "83."  "84."  "85."  "86."  "87."  "88."  "89."  "90." 
##  [91] "91."  "92."  "93."  "94."  "95."  "96."  "97."  "98."  "99."  "100."
    # Turn into numerical values
(rank_data <- as.integer(rank_data))
    ##   [1]   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17
##  [18]  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34
##  [35]  35  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51
##  [52]  52  53  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68
##  [69]  69  70  71  72  73  74  75  76  77  78  79  80  81  82  83  84  85
##  [86]  86  87  88  89  90  91  92  93  94  95  96  97  98  99 100

Title

  • Use SelectorGadget to find the CSS selector .lister-item-header a.

    # Using CSS selectors to scrap the title section
(title_data_html <- html_nodes(webpage, '.lister-item-header a'))
    ## {xml_nodeset (100)}
##  [1] <a href="/title/tt1727824/?ref_=adv_li_tt">Bohemian Rhapsody</a>
##  [2] <a href="/title/tt4530422/?ref_=adv_li_tt">Overlord</a>
##  [3] <a href="/title/tt6966692/?ref_=adv_li_tt">Green Book</a>
##  [4] <a href="/title/tt1477834/?ref_=adv_li_tt">Aquaman</a>
##  [5] <a href="/title/tt5083738/?ref_=adv_li_tt">The Favourite</a>
##  [6] <a href="/title/tt1517451/?ref_=adv_li_tt">A Star Is Born</a>
##  [7] <a href="/title/tt3606756/?ref_=adv_li_tt">Incredibles 2</a>
##  [8] <a href="/title/tt1571234/?ref_=adv_li_tt">Mortal Engines</a>
##  [9] <a href="/title/tt4218572/?ref_=adv_li_tt">Widows</a>
## [10] <a href="/title/tt4532826/?ref_=adv_li_tt">Robin Hood</a>
## [11] <a href="/title/tt6155172/?ref_=adv_li_tt">Roma</a>
## [12] <a href="/title/tt6266538/?ref_=adv_li_tt">Vice</a>
## [13] <a href="/title/tt4633694/?ref_=adv_li_tt">Spider-Man: Into the Spi ...
## [14] <a href="/title/tt4154756/?ref_=adv_li_tt">Avengers: Infinity War</a>
## [15] <a href="/title/tt7349662/?ref_=adv_li_tt">BlacKkKlansman</a>
## [16] <a href="/title/tt2737304/?ref_=adv_li_tt">Bird Box</a>
## [17] <a href="/title/tt4595882/?ref_=adv_li_tt">Can You Ever Forgive Me? ...
## [18] <a href="/title/tt1034415/?ref_=adv_li_tt">Suspiria</a>
## [19] <a href="/title/tt5095030/?ref_=adv_li_tt">Ant-Man and the Wasp</a>
## [20] <a href="/title/tt8359848/?ref_=adv_li_tt">Climax</a>
## ...
    # Converting the title data to text
(title_data <- html_text(title_data_html))
    ##   [1] "Bohemian Rhapsody"                             
##   [2] "Overlord"                                      
##   [3] "Green Book"                                    
##   [4] "Aquaman"                                       
##   [5] "The Favourite"                                 
##   [6] "A Star Is Born"                                
##   [7] "Incredibles 2"                                 
##   [8] "Mortal Engines"                                
##   [9] "Widows"                                        
##  [10] "Robin Hood"                                    
##  [11] "Roma"                                          
##  [12] "Vice"                                          
##  [13] "Spider-Man: Into the Spider-Verse"             
##  [14] "Avengers: Infinity War"                        
##  [15] "BlacKkKlansman"                                
##  [16] "Bird Box"                                      
##  [17] "Can You Ever Forgive Me?"                      
##  [18] "Suspiria"                                      
##  [19] "Ant-Man and the Wasp"                          
##  [20] "Climax"                                        
##  [21] "The Mule"                                      
##  [22] "The Man Who Killed Hitler and Then The Bigfoot"
##  [23] "First Man"                                     
##  [24] "Black Panther"                                 
##  [25] "Hunter Killer"                                 
##  [26] "The Girl in the Spider's Web"                  
##  [27] "Venom"                                         
##  [28] "Bumblebee"                                     
##  [29] "Bad Times at the El Royale"                    
##  [30] "The Ballad of Buster Scruggs"                  
##  [31] "Mary Queen of Scots"                           
##  [32] "Uncle Drew"                                    
##  [33] "Solo: A Star Wars Story"                       
##  [34] "Dragon Ball Super: Broly"                      
##  [35] "A Quiet Place"                                 
##  [36] "Fantastic Beasts: The Crimes of Grindelwald"   
##  [37] "Ready Player One"                              
##  [38] "Arctic"                                        
##  [39] "A Simple Favor"                                
##  [40] "A Private War"                                 
##  [41] "Deadpool 2"                                    
##  [42] "The Front Runner"                              
##  [43] "The Grinch"                                    
##  [44] "Boy Erased"                                    
##  [45] "At Eternity's Gate"                            
##  [46] "Todos lo saben"                                
##  [47] "Tag"                                           
##  [48] "Prospect"                                      
##  [49] "Mary Poppins Returns"                          
##  [50] "Beautiful Boy"                                 
##  [51] "Annihilation"                                  
##  [52] "Crazy Rich Asians"                             
##  [53] "Cold War"                                      
##  [54] "Mission: Impossible - Fallout"                 
##  [55] "If Beale Street Could Talk"                    
##  [56] "Hereditary"                                    
##  [57] "The Nutcracker and the Four Realms"            
##  [58] "Instant Family"                                
##  [59] "Halloween"                                     
##  [60] "Burning"                                       
##  [61] "Ralph Breaks the Internet"                     
##  [62] "Ocean's 8"                                     
##  [63] "The Sisters Brothers"                          
##  [64] "Creed II"                                      
##  [65] "The Predator"                                  
##  [66] "Hotel Transylvania 3: Summer Vacation"         
##  [67] "Replicas"                                      
##  [68] "Stan & Ollie"                                  
##  [69] "The Hate U Give"                               
##  [70] "On the Basis of Sex"                           
##  [71] "Destroyer"                                     
##  [72] "Upgrade"                                       
##  [73] "Red Sparrow"                                   
##  [74] "The Guilty"                                    
##  [75] "The House That Jack Built"                     
##  [76] "Isle of Dogs"                                  
##  [77] "Searching"                                     
##  [78] "The Old Man & the Gun"                         
##  [79] "Nobody's Fool"                                 
##  [80] "Game Night"                                    
##  [81] "Capharnaüm"                                    
##  [82] "Sicario: Day of the Soldado"                   
##  [83] "Dumplin'"                                      
##  [84] "The Meg"                                       
##  [85] "Black Mirror: Bandersnatch"                    
##  [86] "Rampage"                                       
##  [87] "The Nun"                                       
##  [88] "Untogether"                                    
##  [89] "To All the Boys I've Loved Before"             
##  [90] "Then Came You"                                 
##  [91] "Jurassic World: Fallen Kingdom"                
##  [92] "Little Italy"                                  
##  [93] "K.G.F: Chapter 1"                              
##  [94] "Eighth Grade"                                  
##  [95] "Love, Simon"                                   
##  [96] "Peranbu"                                       
##  [97] "High Life"                                     
##  [98] "Mile 22"                                       
##  [99] "Mowgli: Legend of the Jungle"                  
## [100] "Mandy"

Description

  • # Using CSS selectors to scrap the description section
(description_data_html <- html_nodes(webpage, '.ratings-bar+ .text-muted'))
    ## {xml_nodeset (100)}
##  [1] <p class="text-muted">\n    The story of the legendary rock band <a ...
##  [2] <p class="text-muted">\n    A small group of American soldiers find ...
##  [3] <p class="text-muted">\n    A working-class Italian-American bounce ...
##  [4] <p class="text-muted">\n    Arthur Curry, the human-born heir to th ...
##  [5] <p class="text-muted">\n    In early 18th century England, a frail  ...
##  [6] <p class="text-muted">\n    A musician helps a young singer find fa ...
##  [7] <p class="text-muted">\n    The Incredibles hero family takes on a  ...
##  [8] <p class="text-muted">\n    In a post-apocalyptic world where citie ...
##  [9] <p class="text-muted">\n    Set in contemporary Chicago, amid a tim ...
## [10] <p class="text-muted">\n    A war-hardened Crusader and his Moorish ...
## [11] <p class="text-muted">\n    A year in the life of a middle-class fa ...
## [12] <p class="text-muted">\n    The story of <a href="/name/nm0155515"> ...
## [13] <p class="text-muted">\n    Teen Miles Morales becomes Spider-Man o ...
## [14] <p class="text-muted">\n    The Avengers and their allies must be w ...
## [15] <p class="text-muted">\n    Ron Stallworth, an African American pol ...
## [16] <p class="text-muted">\n    Five years after an ominous unseen pres ...
## [17] <p class="text-muted">\n    When Lee Israel falls out of step with  ...
## [18] <p class="text-muted">\n    A darkness swirls at the center of a wo ...
## [19] <p class="text-muted">\n    As Scott Lang balances being both a Sup ...
## [20] <p class="text-muted">\n    French dancers gather in a remote, empt ...
## ...
    # Converting the description data to text
description_data <- html_text(description_data_html)
# take a look at first few
head(description_data)
    ## [1] "\n    The story of the legendary rock band Queen and lead singer Freddie Mercury, leading up to their famous performance at Live Aid (1985)."                                                                         
## [2] "\n    A small group of American soldiers find horror behind enemy lines on the eve of D-Day."                                                                                                                         
## [3] "\n    A working-class Italian-American bouncer becomes the driver of an African-American classical pianist on a tour of venues through the 1960s American South."                                                     
## [4] "\n    Arthur Curry, the human-born heir to the underwater kingdom of Atlantis, goes on a quest to prevent a war between the worlds of ocean and land."                                                                
## [5] "\n    In early 18th century England, a frail Queen Anne occupies the throne and her close friend, Lady Sarah, governs the country in her stead. When a new servant, Abigail, arrives, her charm endears her to Sarah."
## [6] "\n    A musician helps a young singer find fame, even as age and alcoholism send his own career into a downward spiral."
    # strip the '\n'
description_data <- str_replace(description_data, "^\\n\\s+", "")
head(description_data)
    ## [1] "The story of the legendary rock band Queen and lead singer Freddie Mercury, leading up to their famous performance at Live Aid (1985)."                                                                         
## [2] "A small group of American soldiers find horror behind enemy lines on the eve of D-Day."                                                                                                                         
## [3] "A working-class Italian-American bouncer becomes the driver of an African-American classical pianist on a tour of venues through the 1960s American South."                                                     
## [4] "Arthur Curry, the human-born heir to the underwater kingdom of Atlantis, goes on a quest to prevent a war between the worlds of ocean and land."                                                                
## [5] "In early 18th century England, a frail Queen Anne occupies the throne and her close friend, Lady Sarah, governs the country in her stead. When a new servant, Abigail, arrives, her charm endears her to Sarah."
## [6] "A musician helps a young singer find fame, even as age and alcoholism send his own career into a downward spiral."

Runtime

  • # Using CSS selectors to scrap the Movie runtime section
runtime_data_html <- html_nodes(webpage, '.text-muted .runtime')
# Converting the runtime data to text
runtime_data <- html_text(runtime_data_html)
# Let's have a look at the runtime
head(runtime_data)
    ## [1] "134 min" "110 min" "130 min" "143 min" "119 min" "136 min"
    # Data-Preprocessing: removing mins and converting it to numerical
runtime_data <- str_replace(runtime_data, " min", "")
runtime_data <- as.numeric(runtime_data)
#Let's have another look at the runtime data
head(runtime_data)
    ## [1] 134 110 130 143 119 136

Genre

  • # Using CSS selectors to scrap the Movie genre section
genre_data_html <- html_nodes(webpage, '.genre')
# Converting the genre data to text
genre_data <- html_text(genre_data_html)
# Let's have a look at the genre data
head(genre_data)    
    ## [1] "\nBiography, Drama, Music            "   
## [2] "\nAction, Adventure, Horror            " 
## [3] "\nBiography, Comedy, Drama            "  
## [4] "\nAction, Adventure, Fantasy            "
## [5] "\nBiography, Comedy, Drama            "  
## [6] "\nDrama, Music, Romance            "
    # Data-Preprocessing: retrieve the first word
genre_data <- str_extract(genre_data, "[:alpha:]+")
# Convering each genre from text to factor
#genre_data <- as.factor(genre_data)
# Let's have another look at the genre data
head(genre_data)
    ## [1] "Biography" "Action"    "Biography" "Action"    "Biography" "Drama"

Rating

  • # Using CSS selectors to scrap the IMDB rating section
rating_data_html <- html_nodes(webpage, '.ratings-imdb-rating strong')
# Converting the ratings data to text
rating_data <- html_text(rating_data_html)
# Let's have a look at the ratings
head(rating_data)
    ## [1] "8.2" "6.9" "8.3" "7.4" "7.8" "7.9"
    # Data-Preprocessing: converting ratings to numerical
rating_data <- as.numeric(rating_data)
# Let's have another look at the ratings data
rating_data
    ##   [1] 8.2 6.9 8.3 7.4 7.8 7.9 7.8 6.2 7.1 5.3 7.9 7.1 8.7 8.5 7.5 6.7 7.3
##  [18] 7.0 7.1 7.4 7.2 5.6 7.4 7.4 6.6 6.1 6.8 7.1 7.2 7.3 6.5 5.7 7.0 8.3
##  [35] 7.6 6.8 7.5 7.3 6.9 6.7 7.8 6.3 6.3 7.0 7.0 7.0 6.6 6.3 7.2 7.3 6.9
##  [52] 7.0 7.7 7.8 7.6 7.3 5.5 7.6 6.7 7.7 7.3 6.2 7.0 7.6 5.4 6.3 5.4 7.6
##  [69] 7.1 6.5 6.7 7.6 6.6 7.6 7.0 7.9 7.7 6.8 4.4 7.0 8.3 7.1 6.8 5.7 7.4
##  [86] 6.1 5.4 6.1 7.3 7.1 6.2 5.7 8.7 7.5 7.7 9.8 6.7 6.1 6.5 6.6

Votes

  • # Using CSS selectors to scrap the votes section
votes_data_html <- html_nodes(webpage, '.sort-num_votes-visible span:nth-child(2)')
# Converting the votes data to text
votes_data <- html_text(votes_data_html)
# Let's have a look at the votes data
head(votes_data)
    ## [1] "244,241" "29,878"  "60,794"  "163,687" "50,581"  "176,405"
    # Data-Preprocessing: removing commas
votes_data <- str_replace(votes_data, ",", "")
# Data-Preprocessing: converting votes to numerical
votes_data <- as.numeric(votes_data)
#Let's have another look at the votes data
votes_data
    ##   [1] 244241  29878  60794 163687  50581 176405 175164  37925  41656  23638
##  [11]  72984  30559  94428 583976 104499 180681  11418  23607 193639  11827
##  [21]  18515   1292  94010 459800  23478  18243 231555  50103  65200  69737
##  [31]   9680   8122 203565  11109 273314 117735 277320   1170  67621   4737
##  [41] 346678   2976  23125  11324   6157  11201  71218   2192  32848  21356
##  [51] 210740  77897  22080 213750   9628 130083  13550  10211  75064  13550
##  [61]  45918 126606  19738  35130  82787  36267   9573   6103  12276   4012
##  [71]   3505  89637 127544  17452  24709  95930  78972  18521   2242 149057
##  [81]   4708  82980  14647  97179  79774 105649  78155    433  53870    709
##  [91] 197317   4497  17772  33425  74808   8218   1231  40887  39310  37007

Director

  • # Using CSS selectors to scrap the directors section
(directors_data_html <- html_nodes(webpage,'.text-muted+ p a:nth-child(1)'))
    ## {xml_nodeset (100)}
##  [1] <a href="/name/nm0001741/?ref_=adv_li_dr_0">Bryan Singer</a>
##  [2] <a href="/name/nm1170339/?ref_=adv_li_dr_0">Julius Avery</a>
##  [3] <a href="/name/nm0268380/?ref_=adv_li_dr_0">Peter Farrelly</a>
##  [4] <a href="/name/nm1490123/?ref_=adv_li_dr_0">James Wan</a>
##  [5] <a href="/name/nm0487166/?ref_=adv_li_dr_0">Yorgos Lanthimos</a>
##  [6] <a href="/name/nm0177896/?ref_=adv_li_dr_0">Bradley Cooper</a>
##  [7] <a href="/name/nm0083348/?ref_=adv_li_dr_0">Brad Bird</a>
##  [8] <a href="/name/nm0729514/?ref_=adv_li_dr_0">Christian Rivers</a>
##  [9] <a href="/name/nm2588606/?ref_=adv_li_dr_0">Steve McQueen</a>
## [10] <a href="/name/nm1163264/?ref_=adv_li_dr_0">Otto Bathurst</a>
## [11] <a href="/name/nm0190859/?ref_=adv_li_dr_0">Alfonso Cuarón</a>
## [12] <a href="/name/nm0570912/?ref_=adv_li_dr_0">Adam McKay</a>
## [13] <a href="/name/nm2130108/?ref_=adv_li_dr_0">Bob Persichetti</a>
## [14] <a href="/name/nm0751577/?ref_=adv_li_dr_0">Anthony Russo</a>
## [15] <a href="/name/nm0000490/?ref_=adv_li_dr_0">Spike Lee</a>
## [16] <a href="/name/nm0081540/?ref_=adv_li_dr_0">Susanne Bier</a>
## [17] <a href="/name/nm1716636/?ref_=adv_li_dr_0">Marielle Heller</a>
## [18] <a href="/name/nm0345174/?ref_=adv_li_dr_0">Luca Guadagnino</a>
## [19] <a href="/name/nm0715636/?ref_=adv_li_dr_0">Peyton Reed</a>
## [20] <a href="/name/nm0637615/?ref_=adv_li_dr_0">Gaspar Noé</a>
## ...
    # Converting the directors data to text
directors_data <- html_text(directors_data_html)
# Let's have a look at the directors data
head(directors_data)
    ## [1] "Bryan Singer"     "Julius Avery"     "Peter Farrelly"  
## [4] "James Wan"        "Yorgos Lanthimos" "Bradley Cooper"
    # Data-Preprocessing: converting directors data into factors
(directors_data <- as.factor(directors_data))
    ##   [1] Bryan Singer          Julius Avery          Peter Farrelly       
##   [4] James Wan             Yorgos Lanthimos      Bradley Cooper       
##   [7] Brad Bird             Christian Rivers      Steve McQueen        
##  [10] Otto Bathurst         Alfonso Cuarón        Adam McKay           
##  [13] Bob Persichetti       Anthony Russo         Spike Lee            
##  [16] Susanne Bier          Marielle Heller       Luca Guadagnino      
##  [19] Peyton Reed           Gaspar Noé            Clint Eastwood       
##  [22] Robert D. Krzykowski  Damien Chazelle       Ryan Coogler         
##  [25] Donovan Marsh         Fede Alvarez          Ruben Fleischer      
##  [28] Travis Knight         Drew Goddard          Ethan Coen           
##  [31] Josie Rourke          Charles Stone III     Ron Howard           
##  [34] Tatsuya Nagamine      John Krasinski        David Yates          
##  [37] Steven Spielberg      Joe Penna             Paul Feig            
##  [40] Matthew Heineman      David Leitch          Jason Reitman        
##  [43] Yarrow Cheney         Joel Edgerton         Julian Schnabel      
##  [46] Asghar Farhadi        Jeff Tomsic           Christopher Caldwell 
##  [49] Rob Marshall          Felix van Groeningen  Alex Garland         
##  [52] Jon M. Chu            Pawel Pawlikowski     Christopher McQuarrie
##  [55] Barry Jenkins         Ari Aster             Lasse Hallström      
##  [58] Sean Anders           David Gordon Green    Chang-dong Lee       
##  [61] Phil Johnston         Gary Ross             Jacques Audiard      
##  [64] Steven Caple Jr.      Shane Black           Genndy Tartakovsky   
##  [67] Jeffrey Nachmanoff    Jon S. Baird          George Tillman Jr.   
##  [70] Mimi Leder            Karyn Kusama          Leigh Whannell       
##  [73] Francis Lawrence      Gustav Möller         Lars von Trier       
##  [76] Wes Anderson          Aneesh Chaganty       David Lowery         
##  [79] Tyler Perry           John Francis Daley    Nadine Labaki        
##  [82] Stefano Sollima       Anne Fletcher         Jon Turteltaub       
##  [85] David Slade           Brad Peyton           Corin Hardy          
##  [88] Emma Forrest          Susan Johnson         Peter Hutchings      
##  [91] J.A. Bayona           Donald Petrie         Prashanth Neel       
##  [94] Bo Burnham            Greg Berlanti         Ram                  
##  [97] Claire Denis          Peter Berg            Andy Serkis          
## [100] Panos Cosmatos       
## 100 Levels: Adam McKay Alex Garland Alfonso Cuarón ... Yorgos Lanthimos

Actor

  • # Using CSS selectors to scrap the actors section
(actors_data_html <- html_nodes(webpage, '.lister-item-content .ghost+ a'))
    ## {xml_nodeset (100)}
##  [1] <a href="/name/nm1785339/?ref_=adv_li_st_0">Rami Malek</a>
##  [2] <a href="/name/nm5381254/?ref_=adv_li_st_0">Jovan Adepo</a>
##  [3] <a href="/name/nm0001557/?ref_=adv_li_st_0">Viggo Mortensen</a>
##  [4] <a href="/name/nm0597388/?ref_=adv_li_st_0">Jason Momoa</a>
##  [5] <a href="/name/nm1469236/?ref_=adv_li_st_0">Olivia Colman</a>
##  [6] <a href="/name/nm3078932/?ref_=adv_li_st_0">Lady Gaga</a>
##  [7] <a href="/name/nm0005266/?ref_=adv_li_st_0">Craig T. Nelson</a>
##  [8] <a href="/name/nm2623492/?ref_=adv_li_st_0">Hera Hilmar</a>
##  [9] <a href="/name/nm0205626/?ref_=adv_li_st_0">Viola Davis</a>
## [10] <a href="/name/nm5473782/?ref_=adv_li_st_0">Taron Egerton</a>
## [11] <a href="/name/nm8611957/?ref_=adv_li_st_0">Yalitza Aparicio</a>
## [12] <a href="/name/nm0000288/?ref_=adv_li_st_0">Christian Bale</a>
## [13] <a href="/name/nm4271336/?ref_=adv_li_st_0">Shameik Moore</a>
## [14] <a href="/name/nm0000375/?ref_=adv_li_st_0">Robert Downey Jr.</a>
## [15] <a href="/name/nm0913475/?ref_=adv_li_st_0">John David Washington</a>
## [16] <a href="/name/nm0000113/?ref_=adv_li_st_0">Sandra Bullock</a>
## [17] <a href="/name/nm0565250/?ref_=adv_li_st_0">Melissa McCarthy</a>
## [18] <a href="/name/nm1631269/?ref_=adv_li_st_0">Chloë Grace Moretz</a>
## [19] <a href="/name/nm0748620/?ref_=adv_li_st_0">Paul Rudd</a>
## [20] <a href="/name/nm1154749/?ref_=adv_li_st_0">Sofia Boutella</a>
## ...
    # Converting the gross actors data to text
actors_data <- html_text(actors_data_html)
# Let's have a look at the actors data
head(actors_data)
    ## [1] "Rami Malek"      "Jovan Adepo"     "Viggo Mortensen" "Jason Momoa"    
## [5] "Olivia Colman"   "Lady Gaga"
    # Data-Preprocessing: converting actors data into factors
(actors_data <- as.factor(actors_data))
    ##   [1] Rami Malek            Jovan Adepo           Viggo Mortensen      
##   [4] Jason Momoa           Olivia Colman         Lady Gaga            
##   [7] Craig T. Nelson       Hera Hilmar           Viola Davis          
##  [10] Taron Egerton         Yalitza Aparicio      Christian Bale       
##  [13] Shameik Moore         Robert Downey Jr.     John David Washington
##  [16] Sandra Bullock        Melissa McCarthy      Chloë Grace Moretz   
##  [19] Paul Rudd             Sofia Boutella        Bradley Cooper       
##  [22] Sam Elliott           Ryan Gosling          Chadwick Boseman     
##  [25] Gerard Butler         Claire Foy            Tom Hardy            
##  [28] Hailee Steinfeld      Jeff Bridges          Tim Blake Nelson     
##  [31] Saoirse Ronan         Kyrie Irving          Alden Ehrenreich     
##  [34] Masako Nozawa         Emily Blunt           Eddie Redmayne       
##  [37] Tye Sheridan          Mads Mikkelsen        Anna Kendrick        
##  [40] Rosamund Pike         Ryan Reynolds         Hugh Jackman         
##  [43] Benedict Cumberbatch  Lucas Hedges          Willem Dafoe         
##  [46] Penélope Cruz         Jeremy Renner         Sophie Thatcher      
##  [49] Emily Blunt           Steve Carell          Natalie Portman      
##  [52] Constance Wu          Joanna Kulig          Tom Cruise           
##  [55] KiKi Layne            Toni Collette         Mackenzie Foy        
##  [58] Mark Wahlberg         Jamie Lee Curtis      Ah-in Yoo            
##  [61] John C. Reilly        Sandra Bullock        John C. Reilly       
##  [64] Michael B. Jordan     Boyd Holbrook         Adam Sandler         
##  [67] Alice Eve             Shirley Henderson     Amandla Stenberg     
##  [70] Felicity Jones        Nicole Kidman         Logan Marshall-Green 
##  [73] Jennifer Lawrence     Jakob Cedergren       Matt Dillon          
##  [76] Bryan Cranston        John Cho              Robert Redford       
##  [79] Tiffany Haddish       Jason Bateman         Zain Al Rafeea       
##  [82] Benicio Del Toro      Danielle Macdonald    Jason Statham        
##  [85] Fionn Whitehead       Dwayne Johnson        Demián Bichir        
##  [88] Alice Eve             Lana Condor           Asa Butterfield      
##  [91] Chris Pratt           Hayden Christensen    Yash                 
##  [94] Elsie Fisher          Nick Robinson         Mammootty            
##  [97] Robert Pattinson      Mark Wahlberg         Christian Bale       
## [100] Nicolas Cage         
## 94 Levels: Adam Sandler Ah-in Yoo Alden Ehrenreich ... Zain Al Rafeea

Metascore

  • Be careful with missing data.

    # Using CSS selectors to scrap the metascore section
metascore_data_html <- html_nodes(webpage, '.metascore')
# Converting the runtime data to text
metascore_data <- html_text(metascore_data_html)
# Let's have a look at the metascore 
head(metascore_data)
    ## [1] "49        " "60        " "69        " "55        " "90        "
## [6] "88        "
    # Data-Preprocessing: removing extra space in metascore
metascore_data <- str_replace(metascore_data, "\\s*$", "")
metascore_data <- as.numeric(metascore_data)
metascore_data
    ##  [1] 49 60 69 55 90 88 80 44 84 32 96 61 87 68 83 51 87 64 70 83 58 50 84
## [24] 88 43 43 35 66 60 79 60 57 62 59 82 52 64 71 67 75 66 61 51 70 78 66
## [47] 56 68 66 62 79 74 90 86 87 87 39 57 67 90 71 61 78 66 48 54 19 75 81
## [70] 60 62 67 53 83 42 82 71 80 39 66 75 61 53 46 45 46 45 64 43 51 28 90
## [93] 72 81 38 51 81
    # Lets check the length of metascore data
length(metascore_data)
    ## [1] 97
    # Visual inspection finds 69, 74, 87 don't have metascore
ms <- rep(NA, 100)
ms[-c(85, 93, 96)] <- metascore_data
(metascore_data <- ms)
    ##   [1] 49 60 69 55 90 88 80 44 84 32 96 61 87 68 83 51 87 64 70 83 58 50 84
##  [24] 88 43 43 35 66 60 79 60 57 62 59 82 52 64 71 67 75 66 61 51 70 78 66
##  [47] 56 68 66 62 79 74 90 86 87 87 39 57 67 90 71 61 78 66 48 54 19 75 81
##  [70] 60 62 67 53 83 42 82 71 80 39 66 75 61 53 46 NA 45 46 45 64 43 51 28
##  [93] NA 90 72 NA 81 38 51 81

Gross

  • Be careful with missing data.

    # Using CSS selectors to scrap the gross revenue section
gross_data_html <- html_nodes(webpage,'.ghost~ .text-muted+ span')
# Converting the gross revenue data to text
gross_data <- html_text(gross_data_html)
# Let's have a look at the votes data
head(gross_data)
    ## [1] "$210.67M" "$21.70M"  "$61.68M"  "$328.69M" "$30.29M"  "$208.79M"
    # Data-Preprocessing: removing '$' and 'M' signs
gross_data <- str_replace(gross_data, "M", "")
gross_data <- str_sub(gross_data, 2, 10)
#(gross_data <- str_extract(gross_data, "[:digit:]+.[:digit:]+"))
gross_data <- as.numeric(gross_data)
# Let's check the length of gross data
length(gross_data)
    ## [1] 85
    # Visual inspection finds below movies don't have gross
gs_data <- rep(NA, 100)
gs_data[-c(6, 12, 29, 40, 61, 69, 71, 74, 78, 82, 84:87, 90)] <- gross_data
(gross_data <- gs_data)
    ##   [1] 210.67  21.70  61.68 328.69  30.29     NA 208.79 608.58  15.95  42.39
##  [11]  30.82     NA  45.28 180.04 678.82  48.69   8.55   2.47 216.65 102.64
##  [21]  44.94 700.06  15.77  14.84 213.52 125.84  17.84  16.47     NA  42.47
##  [31] 213.77  30.38 188.02 159.44 137.69   0.16  53.54   1.63 324.59     NA
##  [41]   2.00 270.60   6.79   2.29   0.08  54.55 169.83   7.65  32.73 174.53
##  [51]   2.90 220.16  13.79  44.07  54.86  67.36 159.34   0.70 197.59 139.38
##  [61]     NA   3.14 115.62  51.02 167.51   4.04   4.30  29.72     NA  23.85
##  [71]     NA   1.47  11.87     NA  46.87   0.21   0.09     NA  32.02  26.02
##  [81]  11.28     NA  31.71     NA     NA     NA     NA  69.00   0.74     NA
##  [91]  50.07 145.44  99.35 117.44 417.72   0.99  13.54  40.83  36.11   1.21

Missing entries

Visualizing movie data

Scraping image data from Google

Scraping finance data